Recognition of printed Urdu script in Nastaleeq font by using CNN-BiGRU-GRU Based Encoder-Decoder Framework

Zia, Sohail; Azhar, Muhammad; Lee, Bumshik; Tahir, Adnan; Ferzund, Javed; Murtaza, Fozia; Ali, Moazam

doi:10.1016/j.iswa.2023.200194

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…LSTM and GRU address the issues of gradient explosion and vanishing in RNNs [12], [13], demonstrating strong capabilities in learning nonlinear feature sequences, making them well-suited for handling sequential data. BiGRU, a variant of GRU that processes data bidirectionally, overcomes the limitations of GRU in capturing all available future information without requiring delayed future information [14], [15]. It can fully utilize context information in sequences, acquire more comprehensive feature representations, and better capture long-term dependencies and complex patterns in sequences.…”

Section: Introductionmentioning

confidence: 99%

Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

Luo,

Chen,

Sun

et al. 2024

IEEE Access

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The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.INDEX TERMS BiGRU, parameter optimization, feature selection, marine predators algorithm, heuristic algorithm, strip thickness.

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